Neuropathic pain results from damage to or dysfunction of the nervous system. It is a source of incalculable suffering and remains notoriously difficult to treat despite advances in basic research. Ectopic spiking in primary afferents contributes directly to neuropathic pain by driving central sensitization and by providing abnormal sensory input to the CNS. Accordingly, researchers have spent considerable effort trying to understand ectopic spiking, and indeed, much is now known about which ion channels are expressed in different primary afferents and how those channels are altered under neuropathic conditions. However, changes in ion channel expression or properties do not always have straightforward effects on cellular excitability; for example, a single mutation in Nav1.7 channels has been shown to have opposite effects on excitability depending on the other channels present in the cell (Rush et al. 2006; PNAS 103: 8245-50). This illustrates that cellular excitability is an emergent property that depends on the complex interaction between membrane currents. We argue, therefore, that successful development of new analgesics requires an approach that specifically addresses and accounts for the complex ways in which membrane currents interact. Complex (i.e. nonlinear) inter- actions imply that membrane currents compete, cooperate, or interfere with one another. Deciphering those inter- actions requires computational tools that are foreign to pain research. We propose to import tools from dynamical systems theory and, more importantly, to establish the conceptual framework by which to integrate those tools with experimental approaches. We will demonstrate the utility of our integrated approach by using it to explain how patterns of qualitative, injury-induced changes in neuronal excitability (that are clearly linked with neuropathic pain) arise from aberrant nonlinear interactions between quantitatively altered membrane currents. Our approach is a multidisciplinary one that synergistically combines computer modeling, mathematical analysis, and experiments. Top-down modeling will be used to replicate cellular excitability changes in minimal computer models so that dynamical systems analysis can be used to explain excitability changes on the basis of altered nonlinear interactions. Guided by the theoretical knowledge gained through top-down modeling and analysis, bottom-up modeling will be used to identify which injury-induced changes in specific membrane currents are sufficient to explain cellular hyperexcitability patterns. Furthermore, to establish causal links between the changes predicted by top-down and bottom-up modeling, we will conduct dynamic clamp experi- ments in real neurons from naove and nerve-injured animals to determine which molecular (channel) changes are necessary and sufficient to explain hyperexcitability in large diameter dorsal root ganglion (DRG) neurons. In summary, our focus on nonlinear interactions between membrane currents is novel. Our proposed solution for investigating those interactions using computational tools (which have heretofore been missing from pain research) as part of an integrative, multidisciplinary approach is equally innovative and potentially transformative.